Mobile interactive devices are emerging as the next frontier of personalized computing. Such mobile interactive devices include head-mounted displays (“HMDs”) (e.g., supporting augmented reality) and wearable devices. The widespread adoption of such devices will depend on providing effective input-output (“IO”) modalities such as gestures, touch, and voice. A challenge is providing gesture recognition for mobile, interactive devices. Current technologies employ optical sensing for gesture recognition. Such technologies rely on estimating distances to target objects by measuring the time of flight (“ToF”) in air. ToF is the duration between the time a probe signal is transmitted to the target object and the time the reflected version of the probe signal is received. It is measured as
            2      ⁢                          ⁢      d        c    ,where d is the distance of the target object and c=2.998×108 m/s is the speed of light in air.
Although optical sensors are effective at gesture recognition, they face high-energy costs because of illumination overhead and processing complexities (e.g., capture, synchronization, and analysis). The high energy costs limit their use in mobile interactive devices where energy costs carry a big premium in large part because of the weight and size of the battery. For example, an HMD running on a 1500 mAH (3.8 V) battery may have an IO energy budget of 20% (i.e., 4104 J). If an optical sensor consumes 2.5 W of power, the HMD can support about 500 gestures with each gesture lasting 3 seconds (e.g., IO budget/energy-per-gesture=4104 J/7.5 J).